research-article

Modularization of xcsf for multiple output dimensions

Authors:

Martin V. Butz,

Patrick O. StalphAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1243 - 1250

https://doi.org/10.1145/2001576.2001744

Published: 12 July 2011 Publication History

Abstract

XCSF approximates function surfaces by evolving a suitable clustering of the input space, so that a simple -- typically linear -- predictor yields sufficient accuracy in each cluster. With an increasing number of distinct output dimensions, however, the accuracy of local predictions typically decreases. We analyze the performance of a single XCSF instance and compare it to the performance of a multiple-instance XCSF, where each instance predicts one dimension of the output. We show that dependent on the problem at hand, the multiple-instance XCSF approach is highly advantageous. In particular, we show that the more local linearity structures differ, the more a modularized approximation by multiple XCSF instances pays off. In fact, if modularization is not applied, the problem complexity may increase exponentially in the number of approximately orthogonally-structured output dimensions. To relate these results also to current XCSF application options, we show that the multiple-instance XCSF approach can also be applied to the problem of learning a compact model of the Jacobian of the forward-kinematics of a seven degree of freedom anthropomorphic robot arm for inverse robot arm control in simulation.

References

[1]

E. Bernadó-Mansilla and J. M. Garrell-Guiu. Accuracy-based learning classifier systems: Models, analysis, and applications to classification tasks. Evolutionary Computation, 11:209--238, 2003.

Digital Library

[2]

M. V. Butz. Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design. Springer-Verlag, Berlin Heidelberg, 2006.

[3]

M. V. Butz. Sensomotorische Raumrepräsentationen. Informatik-Spektrum, 31:237--240, 2008.

[4]

M. V. Butz, D. E. Goldberg, P. L. Lanzi, and K. Sastry. Problem solution sustenance in XCS: Markov chain analysis of niche support distributions and the impact on computational complexity. Genetic Programming and Evolvable Machines, 8:5--37, 2007.

Digital Library

[5]

M. V. Butz and O. Herbort. Context-dependent predictions and cognitive arm control with XCSF. Genetic and Evolutionary Computation Conference, GECCO 2008, pages 1357--1364, 2008.

Digital Library

[6]

M. V. Butz, P. L. Lanzi, and S. W. Wilson. Hyper-ellipsoidal conditions in XCS: Rotation, linear approximation, and solution structure. Genetic and Evolutionary Computation Conference, GECCO 2006, pages 1457--1464, 2006.

Digital Library

[7]

M. V. Butz, P. L. Lanzi, and S. W. Wilson. Function approximation with XCS: Hyperellipsoidal conditions, recursive least squares, and compaction. IEEE Transactions on Evolutionary Computation, 12:355--376, 2008.

Digital Library

[8]

M. V. Butz, G. Pedersen, and P. O. Stalph. Learning sensorimotor control structures with XCSF: Redundancy exploitation and dynamic control. Genetic and Evolutionary Computation Conference, GECCO 2009, pages 1171--1178, 2009.

Digital Library

[9]

M. V. Butz, K. L. Reif, and O. Herbort. Bridging the gap: Learning sensorimotor-linked population codes for planning and motor control. International Conference on Cognitive Systems, CogSys 2008, 2008.

[10]

J. Drugowitsch and A. Barry. A formal framework and extensions for function approximation in learning classifier systems. Machine Learning, 70:45--88, 2008.

Digital Library

[11]

J. H. Holland and J. S. Reitman. Cognitive systems based on adaptive algorithms. In D. A. Waterman and F. Hayes-Roth, editors, Pattern directed inference systems, pages 313--329. Academic Press, New York, 1978.

[12]

P. L. Lanzi, D. Loiacono, S. W. Wilson, and D. E. Goldberg. Prediction update algorithms for XCSF: RLS, Kalman filter and gain adaptation. Genetic and Evolutionary Computation Conference, GECCO 2006, pages 1505--1512, 2006.

Digital Library

[13]

P. L. Lanzi, D. Loiacono, S. W. Wilson, and D. E. Goldberg. Generalization in the XCSF classifier system: Analysis, improvement, and extension. Evolutionary Computation, 15:133--168, 2007.

Digital Library

[14]

O. Sigaud and J. Peters, editors. From Motor Learning to Interaction Learning in Robots. Springer, 2010.

Digital Library

[15]

P. O. Stalph, J. Rubinsztajn, O. Sigaud, and M. V. Butz. A comparative study: Function approximation with LWPR and XCSF. In Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, pages 1863--1870. ACM, 2010.

Digital Library

[16]

C. Stone and L. Bull. An analysis of continuous-valued representations for learning classifier systems. In L. Bull and T. Kovacs, editors, Foundations of Learning Classifier Systems, Studies in Fuzziness and Soft Computing, pages 127--175. Springer-Verlag, Berlin Heidelberg, 2005.

[17]

S. Vijayakumar, A. D'Souza, and S. Schaal. Incremental online learning in high dimensions. Neural Computation, 17:2602--2634, 2005.

Digital Library

[18]

S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149--175, 1995.

Digital Library

[19]

S. W. Wilson. Generalization in the XCS classifier system. Genetic Programming 1998: Proceedings of the Third Annual Conference, pages 665--674, 1998.

[20]

S. W. Wilson. Get real! XCS with continuous-valued inputs. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Learning classifier systems: From foundations to applications (LNAI 1813), pages 209--219. Springer-Verlag, Berlin Heidelberg, 2000.

Digital Library

[21]

S. W. Wilson. Function approximation with a classifier system. Genetic and Evolutionary Computation Conference, GECCO 2001, pages 974--981, 2001.

[22]

S. W. Wilson. Classifiers that approximate functions. Natural Computing, 1:211--234, 2002.

Digital Library

Cited By

Nazmi SHomaifar A(2018)Possibility Rule-Based Classification Using Function Approximation2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00122(668-674)Online publication date: Oct-2018
https://doi.org/10.1109/SMC.2018.00122
Chen GDouch CZhang M(2016)Accuracy-Based Learning Classifier Systems for Multistep Reinforcement Learning: A Fuzzy Logic Approach to Handling Continuous Inputs and Learning Continuous ActionsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2016.256013920:6(953-971)Online publication date: Dec-2016
https://doi.org/10.1109/TEVC.2016.2560139
Chen GDouch CZhang M(2015)Using Learning Classifier Systems to Learn Stochastic Decision PoliciesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2015.241546419:6(885-902)Online publication date: Dec-2015
https://doi.org/10.1109/TEVC.2015.2415464
Show More Cited By

Index Terms

Modularization of xcsf for multiple output dimensions

Recommendations

An anticipatory approach to improve XCSF
GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

XCSF is a novel version of learning classifier systems (LCS) which extends the typical concept of LCS by introducing computable classifier prediction. In XCSF Classifier prediction is computed as a linear combination of classifier inputs and a weight ...
Improving generalization in the XCSF classifier system using linear least-squares
GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation

XCSF is an extension of XCS in which classifier prediction is computed as a linear combination of classifier inputs and a weight vector associated to each classifier. XCSF can adjust the weight vector of classifiers to evolve accurate piecewise linear ...
Guided evolution in XCSF
GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

High-dimensional problems are challenging for iterative, online (Michigan-style) Learning Classifier Systems, especially because of the large size of the evolutionary search space. The present work proposes to guide mutation toward most suitable ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO '11

Sponsor:

SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
96
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Nazmi SHomaifar A(2018)Possibility Rule-Based Classification Using Function Approximation2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00122(668-674)Online publication date: Oct-2018
https://doi.org/10.1109/SMC.2018.00122
Chen GDouch CZhang M(2016)Accuracy-Based Learning Classifier Systems for Multistep Reinforcement Learning: A Fuzzy Logic Approach to Handling Continuous Inputs and Learning Continuous ActionsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2016.256013920:6(953-971)Online publication date: Dec-2016
https://doi.org/10.1109/TEVC.2016.2560139
Chen GDouch CZhang M(2015)Using Learning Classifier Systems to Learn Stochastic Decision PoliciesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2015.241546419:6(885-902)Online publication date: Dec-2015
https://doi.org/10.1109/TEVC.2015.2415464
Kneissler JStalph PDrugowitsch JButz M(2014)Filtering sensory information with xcsfEvolutionary Computation10.1162/EVCO_a_0010822:1(139-158)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1162/EVCO_a_00108
Kneissler JStalph PDrugowitsch JButz MMoore J(2012)Filtering sensory information with XCSFProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330284(871-878)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330163.2330284

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten